AI in Vatican City
- AI in Vatican City: Server Configuration
This article details the server infrastructure supporting Artificial Intelligence initiatives within Vatican City. It is geared towards new system administrators and developers involved in maintaining and expanding these systems. This documentation is current as of November 8, 2023.
Overview
The Vatican currently utilizes AI for a variety of tasks, including digital archiving of historical documents (specifically within the Vatican Secret Archives, linguistic analysis of ancient texts, and enhancing security measures. The server infrastructure is designed for high reliability, data integrity, and scalability, while adhering to strict security protocols. The core philosophy is a hybrid approach, leveraging both on-premise hardware and cloud services for specific workloads. This setup allows for control over sensitive data while utilizing the elasticity of cloud computing for less critical processes. See also Data Security Protocols.
Hardware Infrastructure
The primary on-premise server cluster is located within a secure, climate-controlled facility. Redundancy is a key design principle. The cluster consists of the following components:
| Component | Description | Quantity | Specifications |
|---|---|---|---|
| Compute Servers | Hosts AI models and applications. | 8 | Dual Intel Xeon Gold 6338, 128GB DDR4 ECC RAM, 2 x 4TB NVMe SSD (RAID 1) |
| Storage Servers | Stores datasets, model weights, and backups. | 4 | 16 x 16TB SAS HDDs (RAID 6), 256GB DDR4 ECC RAM |
| Network Switches | High-bandwidth connectivity within the cluster. | 2 | Cisco Catalyst 9300 Series, 48-port Gigabit Ethernet, 10G Uplink |
| Firewall | Perimeter security and access control. | 2 (Active/Passive) | Fortinet FortiGate 600F |
| Load Balancer | Distributes traffic across compute servers. | 2 (Active/Passive) | HAProxy |
All servers run a hardened version of Ubuntu Server 22.04 LTS. The network is segmented using Virtual LANs (VLANs) to isolate different workloads and enhance security. Regular Security Audits are conducted.
Software Stack
The software environment is centered around open-source technologies, maximizing flexibility and minimizing licensing costs. Key components include:
| Software | Version | Purpose | Notes |
|---|---|---|---|
| Python | 3.10 | Primary programming language for AI development. | Used with libraries like TensorFlow and PyTorch. |
| TensorFlow | 2.12 | Machine Learning framework. | Accelerated by NVIDIA GPUs. |
| PyTorch | 2.0 | Alternative Machine Learning framework. | Used for research and development. |
| PostgreSQL | 14 | Database for storing metadata and results. | Utilizes WAL archiving for point-in-time recovery. See Database Backups. |
| Docker | 20.10 | Containerization platform. | Simplifies deployment and management of applications. |
| Kubernetes | 1.26 | Container orchestration platform. | Manages scaling and availability of containerized workloads. |
All code is managed using Git and hosted on a private GitLab instance. Continuous integration and continuous deployment (CI/CD) pipelines are implemented for automated testing and deployment.
Cloud Integration
Certain AI workloads, specifically those requiring significant computational resources for short periods, are offloaded to a cloud provider. Currently, Amazon Web Services (AWS) is used.
| Service | Instance Type | Purpose | Region |
|---|---|---|---|
| EC2 | p4d.24xlarge | Training large language models. | us-east-1 |
| S3 | Standard | Storage of large datasets. | us-east-1 |
| SageMaker | Notebook Instances | Interactive development and experimentation. | us-east-1 |
| Lambda | General Purpose | Serverless functions for data processing. | us-east-1 |
Data transfer between the on-premise cluster and AWS is secured using Virtual Private Networks (VPNs) and encryption. Access to AWS resources is strictly controlled using IAM roles and policies. Access logs are reviewed daily. See also Cloud Security Best Practices.
Monitoring and Alerting
Comprehensive monitoring is essential for maintaining the stability and performance of the AI infrastructure. The following tools are used:
- Prometheus: Collects metrics from servers and applications.
- Grafana: Visualizes metrics and creates dashboards.
- Alertmanager: Sends alerts based on predefined rules.
- ELK Stack (Elasticsearch, Logstash, Kibana): Centralized logging and analysis.
Alerts are configured for critical events such as high CPU usage, low disk space, and network errors. On-call engineers are notified via PagerDuty.
Future Considerations
Future plans include exploring the use of specialized AI accelerators, such as NVIDIA’s Hopper architecture, to further improve performance. We are also investigating the potential of federated learning to enable collaborative AI development while preserving data privacy. Further documentation will be available on the AI Development Wiki.
Server Maintenance Network Configuration Disaster Recovery Plan Data Retention Policy User Account Management
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️